Driven by technological advancements in generative AI and the shortage of data professionals in the European labour market, a growing interest in data science education has led to the development of numerous data science curricula. However, a standardized competency framework for data scientists has not yet been established. Moreover, data scientists have shifted from a generalist approach to focusing on specialised roles within the data ecosystem. As a result, data science curricula have become more specialised, often including a comprehensive introductory phase followed by in-depth studies in specific areas. However, many students struggle to combine the diverse competencies and knowledge elements a data science degree teaches. To address this challenge, this research project focuses on developing a data science framework that identifies interdependencies between competencies and knowledge elements, enabling students to choose personalized learning paths based on their individual goals and prior knowledge. This paper introduces a competency network to create personalized learning paths for an introductory data science course. It will be based on professionally logical interdependencies, which will be evaluated and optimised through the analysis of expert interviews. The goal is to positively impact students' self-efficacy, motivation, and learning outcomes by providing a structured and adaptable learning experience.

This working group aims to develop a research-based AI concept inventory (AI CI) to assess the understanding of foundational AI concepts among non-experts. By identifying core concepts and common misconceptions through literature reviews, expert consultations, and iterative validation, the group will create a user-friendly assessment tool that can be used to capture snapshots of AI understanding, support benchmarking across contexts, and inform educational initiatives and policy. Designed for diverse non-expert audiences, including educators, students, and the general public, this tool can provide valuable insights into how AI knowledge evolves over time, contributing to the broader goal of promoting AI literacy in everyday contexts.

Most computing students enter the industry once they graduate. As future software engineers, they will be in powerful positions, making decisions that impact their personal lives, others, and society. Thus, preparing graduates for their careers is crucial by addressing ethical considerations, decision problems, and other concepts related to morals, values, and legal aspects (e.g., data protection, privacy, security, etc.) as part of computing curricula. In this paper, we propose the integration of data ethics into computing programs and provide a framework for an ethics module, including relevant competency-based learning objectives. The proposed module is based on a curricular analysis of all 71 German data science degree programs focusing on ethics courses. The course contents and competency goals were analyzed and classified based on their cognitive complexity. As the results proved the lack of competency-based learning outcomes, we designed observable competency goals, meaning knowledge, skills, and dispositions taken in the context of a task. In addition, we provide suggestions for contents, pedagogical instructions, and assessments in such a course. The proposed module serves as a first draft and resource to support other educators aiming to design such a course and who are willing to integrate it into computing curricula.

Generative AI (GenAI) has seen great advancements in the past two years and the conversation around adoption is increasing. Widely available GenAI tools are disrupting classroom practices as they can write and explain code with minimal student prompting. While most acknowledge that there is no way to stop students from using such tools, a consensus has yet to form on how students should use them if they choose to do so. At the same time, researchers have begun to introduce new pedagogical tools that integrate GenAI into computing curricula. These new tools offer students personalized help or attempt to teach prompting skills without undercutting code comprehension. This working group aims to detail the current landscape of education-focused GenAI tools and teaching approaches, present gaps where new tools or approaches could appear, identify good practice-examples, and provide a guide for instructors to utilize GenAI as they continue to adapt to this new era.

The project Knowledge Graphs for competency-based Education (KG4CBE) conducts educational data science research to establish competency-based instruction in higher-education programs. In this paper, we propose the design of a knowledge graph to examine the impact of instructional design on student-teacher interaction to facilitate complex learning. For this purpose, the knowledge graph will incorporate the components of an online introductory data science course, including educational materials and learning tasks created with the Four Component Instructional Design (4C/ID) model. Furthermore, the knowledge graph will incorporate data recording the behaviors, interactions and assessments of participating students. To study the competency-based instruction process, the proposed knowledge graph must be scalable to the big data quantities common in educational settings. Therefore, the knowledge graph will be deployed as a tool with accompanying routines to acquire, simulate and load educational data. Furthermore, this tool will provide methods to interact with and visualize the stored information. As future research, we aim to evaluate the proposed knowledge graph in a large-scale educational design research study, to examine the impact of monitoring, forecasting and recommendations in complex learning settings.